E-commerce has thrived as Internet technology has developed. E-commerce uses computer technology, network technology, and remote communications technology to enable buyers and sellers in commercial transactions to carry out various commercial activities without having to meet in person. In order to carry out a commercial transaction, a buyer must gain prior knowledge of the content information concerning a product, information that will help him or her decide whether or not to buy the product. The product content information comprises the product categories under which the product is classified, the supplier of the product, product price, and other types of information relating to the product. On an e-commerce information transaction platform, there are two main methods of acquiring said content information. The first method is a search conducted on the initiative of the user (buyer). That is, after determining their own purchase needs, users put their requests into the form of keywords and then use the keywords to perform searches in vast quantities of diverse data and thereby acquire the content information that they need. The other method is the user passive receiving model. That is, the seller recommends product content information to users through e-commerce transaction platforms. After passively receiving seller-recommended product content information, they purchase the appropriate products under the guidance of this content information. With regard to the second method, the seller in the transaction, seeking to increase its transaction success rate, often will not recommend all of its own information to users, but first analyzes past user behavior and establishes user preference data. Then, in accordance with user preferences, it recommends, in a targeted manner, specific information that might be of interest to users. Such a recommendation method can significantly improve users' experiences in e-commerce information transactions. It can increase the accuracy of seller exposure and effectively lead users into becoming buyers, thereby lowering transaction costs. However, when the recommender of the e-commerce information analyzes user preferences, the user behavior historical data which it obtains might contain biases and errors and might even include fraudulent data that pretends to be user activities but in fact are generated for the purposes of misguiding how products are recommended to the user. The analytical results established on such data foundations inevitably fail to reflect true user preference characteristics, with the result that recommended content information deviates from user needs and recommendation results are adversely affected. In addition, because especially huge volumes of information are accessed at e-commerce websites and vast quantities of user behavior data are found at e-commerce website servers, the analysis of vast quantities of user behavior data to obtain user preference data is severely trying on the processing capabilities of the recommendation system itself. The vast quantities of user behavior data slow the analytical processing speed of the recommendation system.